Preface.
CONTENS
1. Introduction.
Part I Foundations.
2. A Gentle Start.
3. A Formal Learning Model.
4. Learning Via Uniform Convergence.
5. The Bias-Complexity Trade-Off.
6. The CV Dimension.
7. Nonuniform Learnability.
8. The Runtime of Learning.
Part 2 From Theory to algorithms.
9. Linear Predictors.
10. Boosting.
11. Model Selection and Validation.
12. Convex Learning Problems.
13. Regularization and Stability.
14. Stochastic Gradient Descent.
15. Support Vector Machines.
16. Kernel methos.
17. Multiclass, Ranking, and Complex Prediction Problems.
18. Decision Trees.
19. Nearest Neighbour.
20. Neural Networks.
Part III Additional Learning Model.
21. Online Learning.
22. Clustering.
23. Dimensionality Reduction.
24. Generative Models.
25. Feature Selection and Generation.
Part IV Advanced Theory.
26. Rademacher Complexities.
27. Covering Numbers.
28. Proof of the Fundamentals.
29. Multiclass Learnability.
30. Compression Bounds.
31. PAC-Bayes.
Appendix A Technical Lemmas.
Appendix B Measure Concentration.
Appendix C Linear Algebra.
Reference.
Index.